Data mining of Bayesian networks using cooperative coevolution

Decision Support Systems

Volumn 38, Issue 3, 2004, Pages 451-472

  Wong, Man Leung ^a   Lee, Shing Yan ^b   Leung, Kwong Sak ^b  

^a LINGNAN UNIVERSITY   (Hong Kong)

^b CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Bayesian networks; Cooperative coevolution; Data mining; Evolutionary computation

Indexed keywords

BACKPROPAGATION; GENETIC ALGORITHMS; GRAPHIC METHODS; KNOWLEDGE REPRESENTATION; LEARNING ALGORITHMS; MATHEMATICAL MODELS; NEURAL NETWORKS; PROBLEM SOLVING;

BAYESIAN NETWORKS; COOPERATIVE COEVOLUTION; DATA SETS; EVOLUTIUONARY COMPUTATION;

DATA MINING;

EID: 4544296304     PISSN: 01679236     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-9236(03)00115-5     Document Type: Article

References (57)

1. Agresti A. Categorical data analysis. Circa. 1990;Wiley, New York.
2. Bäck T. Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming. Genetic Algorithms. 1996;Oxford Univ. Press, New York.
3. Bäck T., Fogel D.B., Michalwicz Z. Evolutionary Computation: 1. Basic Algorithms and Operators. 2000;Institute of Physics Publishing, Bristol.
4. Bäck T., Fogel D.B., Michalwicz Z. Evolutionary Computation: 1. Advanced Algorithms and Operators. 2000;Institute of Physics Publishing, Bristol.
5. Beasley D., Bull D.R., Martin R.R. An Overview of Genetic Algorithms: Part 1. Fundamentals. University Computing. 15(2):1993;58-69 ftp://ralph.cs.cf.ac.uk/pub/papers/GAs/ga/_overview1.ps.
6. Beaumont G.P., Knowles J.D. Statistical Tests: An Introduction with MINITAB Commentary. 1996;Prentice-Hall, London.
7. Beinlinch I., Suermondt H., Chavez R., Cooper G. The ALARM monitoring system: a case study with two probabilistic inference techniques for belief networks. Proceedings of Second European Conference Artificial Intelligence in Medicine. 1989;247-256.
8. Bhattacharyya S. Direct Marketing Response Models using Genetic Algorithms. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining. 1998;144-148.
9. Bhattacharyya S. Evolutionary algorithms in data mining: multi-objective performance modeling for direct marketing. Proceedings of the Sixth International Conference on Knowledge Discovery and Data Mining. 2000 (August);465-473.
10. Bayesian Network tools in Java (BNJ) http://bndev.sourceforge.net.
11. R.R. Bouckaert, Bayesian Belief Networks: from Inference to Construction, PhD thesis, Utrecht University, June 1995.
12. P. Brierley, Tiberius: Viaual Neural Data Mining, http://www. philbrierley.com.
13. Cabena P., Hadjinian P., Stadler R., Verhees J., Zansi A. Discovering Data Mining: From Concept to Implementation. 1997;Prentice-Hall, NJ.
14. Cheng J., Greiner R., Kelly J., Bell D., Liu W. Learning Bayesian network from data: an information-theory based approached. Artificial Intelligence. 137:2002;43-90.
15. D.M. Chickering, D. Geiger, D. Heckerman, Learning Bayesian Network is NP-Hard. Tech. rep., Microsoft Research, 1994.
16. Cooper G.F. A simple constraint-based algorithm for efficiently mining observational Databases for Causal Relationships. Data Mining and Knowledge Discovery. 1:1997;203-224.
17. Dash D., Druzdzel M.J. A hybrid anytime algorithm for the construction of causal models from sparse data. Laskey K.B., Prade H. Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (Stockholm, Sweden, July-August 1999). 1999;142-149 Morgan Kaufmann, CA.
18. L.M. de Campos, J.F. Huete, Approximating causal orderings for Bayesian networks using genetic algorithms and simulated annealing. Tech. rep., Department of Computer Science and Artificial Intelligence, University of Granada, May 1999.
19. Fayyad U.M., Piatetsky-Shapiro G., Smyth P. From data mining to knowledge discovery: an overview. Advances in Knowledge Discovery in Data Mining. 1996;1-34 AAAI Press, Menlo Park, CA.
20. Fogel D.B. Evolutionary Computation: Toward a New Philosophy. 2000;IEEE Press, NY.
21. Fogel L., Owens A., Walsh M. Artificial Intelligence Through Simulated Evolution. 1966;Wiley, NY.
22. Fung R.M., Crawford S.L. Constructor: a system for the induction of probabilistic models. Proceedings of the Seventh National Conference on Artificial Intelligence. 1990;762-769.
23. Goldberg D.E. Genetic Algorithms in Search, Optimization, and Machine Learning. 1989;Addison-Wesley, MA.
24. D. Heckerman, A tutorial on learning Bayesian networks, Tech. rep., Microsoft Research, Advanced Technology Division, March 1995.
25. Heckerman D. Bayesian Networks for data mining. Data Mining and Knowledge Discovery. 1:1997;79-119.
26. Heckerman D., Horvitz E. Inferring informational goals from free-text queries: a Bayesian approach. Proceedings of Fourteenth Conference of Uncertainty in Artificial Intelligence(Wisconsin, USA, July 1998). 1998;230-237 Morgan Kaufmann, CA.
27. Heckerman D., Wellman M.P. Bayesian networks. Communications of the ACM. 38(3):1995 (March);27-30.
28. Heckerman D.E., Horvitz E.J., Nathwani B.N. Toward normative expert systems: Part I. The Pathfinder project. Methods of Information in Medicine. 31(2):1992;90-105.
29. Henrion M. Propagating uncertainty in Bayesian networks by logic sampling. Lemmar J., Kanal L. Proceedings of the Second Conference on Uncertainty in Artificial Intelligence (Amsterdam North Holland, 1988). 1988;149-163.
30. Herskovits E.H., Cooper G.F. A Bayesian method for the induction of probabilistic networks from data. Machine Learning. 9(4):1992;309-347.
31. Holland J.H. Adaptation in Natural and Artificial Systems. 1975;University of Michigan Press, MI.
32. Hugin Expert, http://www.hugin.com.
33. Koza J.R. Genetic Programming: On the Programming of Computers by Means of Natural Selection. 1992;MIT Press, MA.
34. Lam W., Bacchus F. Learning Bayesian belief networks - an approach based on the MDL principle. Computational Intelligence. 10(4):1994;269-293.
35. Larrañaga P., Poza M., Yurramendi Y., Murga R., Kuijpers C. Structural learning of Bayesian network by genetic algorithms: a performance analysis of control parameters. IEEE Transactions on Pattern Analysis and Machine Intelligence. 8(9):1996 (September);912-926.
36. Ling C.X., Li C. Data mining for direct marketing: problems and solutions. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining. 1998;73-79.
37. Msbnx: Microsoft Belief Network Tools, http://www.research.microsoft.com/ adapt/MSBNx.
38. Neapolitan R. Probabilistic Reasoning in Expert Systems. 1990;Wiley, New York.
39. Olesen K.G., Kjærulff U., Jensen F., Jense F.V., Falck B., Andreassen S., Andersen S.K. A MUNIN network for the median nerve - a case study in loops. Applied Artificial Intelligence. 3:1989;385-404.
40. Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. 1988;Morgan Kaufmann, CA.
41. Petrison L.A., Blattberg R.C., Wang P. Database marketing: past present, and future. Journal of Direct Marketing. 11(4):1997;109-125.
42. M.A. Potter, The design and analysis of a computational model of cooperative coevolution, PhD Thesis, George Mason University, 1997.
43. Potter M.A., DeJong K.A. Evolving neural networks with collaborative species. Proceedings of the 1995 Summer Computer Simulation Conference (Ottawa, Canada, 1995):1995.
44. Potter M.A., DeJong K.A., Grefenstette J. A coevolutionary approach to learning sequential decision rules. Proceedings of the Sixth International Conference (ICGA'95). 1995 (July);366-372.
45. Rissanen J. Modelling by shortest data description. Automatica. 14:1978;465-471.
46. Rud O.P. Data Mining Cookbook: Modeling Data for Marketing, Risk and Customer Relationship Management. 2001;Wiley, New York.
47. Silverstein C., Brin S., Motwani R., Ullman J. Scalable techniques for mining causal structures. Data Mining and Knowledge Discovery. 4:2000;163-192.
48. M. Singh, Learning Bayesian Networks for Solving Real-World Problems, PhD Thesis, University of Pennsylvania, 1998.
49. C. Spatz, J.O. Johnston, Basic statistics: tales of distribution, 2nd ed. Brooks/Cole Publishing, Monterey, CA, 1981.
50. Spirtes P., Glymour C., Scheines R. Causation, Prediction, and Search. 2nd ed. 2000;MIT Press, MA.
51. Suzuki J. Learning Bayesian belief networks based on the minimum description length principle: basic properties. IEICE Transactions Fundamentals. vol. E82-A:1999 (November).
52. Tian J. A branch-and-bound algorithm for MDL learning Bayesian networks. Boutilier C., Goldszmidt M. Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, (San Francisco, CA, June-July 2000). 2000;580-588 Morgan Kaufmann, CA.
53. Wong M.L., Lam W., Leung K.S. Using evolutionary programming and minimum description length principle for data mining of Bayesian networks. IEEE Transactions on Pattern Analysis and Machine Intelligence. 21(2):1999 (February);174-178.
54. Wong M.L., Lam W., Leung K.S., Ngan P.S., Cheng J.C.Y. Discovering knowledge from medical databases using evolutionary algorithms. IEEE Engineering in Medicine and Biology Magazine. 19(4):2000;45-55.
55. Xiang Y., Chu T. Parallel learning of belief networks in large and difficult domains. Data Mining and Knowledge Discovery. 3:1999;315-339.
56. Zahavi J., Levin N. Applying neural computing to target marketing. Journal of Direct Marketing. 11(4):1997;76-93.
57. Zahavi J., Levin N. Issues and problems in applying neural computing to target marketing. Journal of Direct Marketing. 11(4):1997;63-75